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Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

Jure Demšar¹, Iztok Lebar Bajec¹

¹Faculty of Computer and Information Science, Večna Pot 113, 1000 Ljubljana, Slovenia.

|January 4, 2017

Summary

This study introduces a novel artificial life model that evolves collective behaviors in agents. The model successfully generates swarming, milling, and polarized behaviors, offering insights into the evolution of social dynamics.

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Area of Science:

* Computational biology
* Artificial life
* Evolutionary modeling

Background:

* Collective behavior is widely observed in nature and studied using computational models.
* Previous models often focus on specific aspects or tune parameters, rarely evolving behavior from scratch.
* Existing evolutionary models have primarily shown clumping or swarming, but empirical data suggests more complex behaviors exist in groups like fish.

Purpose of the Study:

* To investigate the evolutionary pressures driving collective behavior.
* To develop a novel evolutionary model capable of generating diverse collective behaviors.
* To explore the evolution of swarming, milling, and polarized behaviors from basic principles.

Main Methods:

* Development of an artificial life-like evolutionary model.
* Agents governed by linguistic fuzzy rule-based systems.
* Evolution of collective behaviors from initial conditions.

Main Results:

* The model successfully evolved all three observed classes of fish school behavior: swarming, milling, and polarized.
* This demonstrates the capability of the linguistic fuzzy rule-based system to generate complex emergent behaviors.
* The model provides a framework for studying the evolution of diverse collective behaviors.

Conclusions:

* The novel evolutionary model can generate multiple complex collective behaviors, including swarming, milling, and polarized movement.
* Linguistic fuzzy rule-based systems are effective for evolving sophisticated group dynamics in artificial life.
* This approach offers new avenues for understanding the evolutionary origins of collective behavior in biological systems.